Gaist Case Study: AI-Powered Highway Condition Analysis

Gaist was founded in 2007, leveraging considerable knowledge in infrastructure surveying to better understand the condition of highway infrastructure and assets, enabling detailed lifecycle planning to be undertaken in a way that was not commonplace. Gaist has a reputation for developing highly intelligent systems that challenge legacy technology and methodologies, employing the next generation of AI within an award-winning platform for organisations both in the UK and globally — including local authorities, central government departments, software asset management companies, utilities suppliers, private infrastructure owners and highway maintenance contractors.

Project Background

The team at Gaist has always been passionate about utilising the power of data and machine learning technology to help with its ambitious mission to build the world's deepest and most sophisticated understanding of the roadscape environment. To that end, the company has its own AI Innovation Hub in North Yorkshire, where in-house computer scientists and research specialists work to deploy the latest deep learning research techniques to accelerate the development of tools, techniques and intellectual property.

360-degree panoramic road survey imagery captured by Gaist's fleet of mapping vehicles showing highway condition data

Using its fleet of mapping vehicles, Gaist captures between 40–60 million images per month. Roof-mounted 360° 30-megapixel panoramic cameras survey the environment every 2–5m, whilst carriageway 4K cameras capture road data every 1m.

Project Approach

In order to provide carriageway condition data of unprecedented accuracy and quality, Gaist requires powerful GPU-accelerated systems to train its models to recognise 35 damage types including potholes, cracks and uneven surfaces. GPU-accelerated workstations were needed to handle huge volumes of data and reduce bottlenecks in the analysis pipeline.

NVIDIA RTX AI development workstations built by Scan's 3XS Systems division for Gaist's AI Innovation Hub

Following a testing period of various configurations, NVIDIA professional GPUs were chosen for their robust performance and high degree of support.

Project Results

Conducting image analysis using the GPU-accelerated systems proved key to reducing pipeline bottlenecks, ensuring Gaist could process data in a timely manner to keep pace with the influx of new images daily.

Gaist AI platform output showing road damage classification results including pothole and crack detection across a carriageway survey

The Scan Partnership

Scan worked with Gaist to provide a variety of custom-built GPU-accelerated systems designed by our in-house 3XS Systems division. These systems range from NVIDIA Data Science Workstations powered by RTX A4000 and RTX A5000 GPUs for model development, to systems with Tesla T4 GPUs used for inferencing. This GPU-accelerated hardware has helped form part of Gaist's AI Innovation Hub at its headquarters in North Yorkshire.

Project wins

GPU acceleration of image identification and classification across 40–60 million images per month

Time and cost savings generated due to rapid image recognition and reduced pipeline bottlenecks

Steve Birdsall

CEO, Gaist

"Many companies collect data but few can turn that data into meaningful action. Our world-class team uses its deep understanding of our clients' needs to deliver valuable insights that help streamline operations, reduce risk and cut costs while increasing safety."

Speak to an Expert

You've seen how Scan helped Gaist develop its road quality analysis solution. Contact our expert AI team to discuss your project requirements.

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Gaist Case Study - AI-Powered Road Survey and Highway Condition Analysis

Gaist